[codex] add image apps and semantic tab resizing

+labkit/+ui/+app/createShell.m

@@ -58,11 +58,9 @@
 function tabs = standardTabs()
     tabs = [ ...
         labkit.ui.app.tab('filesAnalysis', 'Files + Analysis', [3 1], ...
-            {260, 'fit', 'fit'}, ...
-            struct('resizeRows', [1 2])), ...
+            {260, 'fit', 'fit'}), ...
         labkit.ui.app.tab('summaryResults', 'Summary + Results', [2 1], ...
-            {'fit', '1x'}, ...
-            struct('resizeRows', 1)), ...
+            {'fit', '1x'}), ...
         labkit.ui.app.tab('log', 'Log', [1 1], {'1x'})];
 end
 

+labkit/+ui/+app/private/createTabbedWorkbenchShell.m

@@ -142,10 +142,29 @@ function enableScrollableGrid(grid)
 
 function rows = validResizeRows(spec, logicalRows)
     rows = [];
-    if ~isfield(spec, 'resizeRows') || isempty(spec.resizeRows)
+    if isfield(spec, 'resizeRows') && ~isempty(spec.resizeRows)
+        rows = unique(spec.resizeRows(:).');
+        rows = rows(rows >= 1 & rows < logicalRows & isfinite(rows));
         return;
     end
-    rows = unique(spec.resizeRows(:).');
+
+    mode = optionValue(spec, 'resize', 'betweenRows');
+    if islogical(mode)
+        if mode
+            rows = 1:max(logicalRows - 1, 0);
+        end
+        return;
+    end
+    mode = lower(char(string(mode)));
+    switch mode
+        case {'betweenrows', 'auto', 'all'}
+            rows = 1:max(logicalRows - 1, 0);
+        case {'none', 'off', 'false'}
+            rows = [];
+        otherwise
+            error('labkit:ui:InvalidTabResizeMode', ...
+                'Unsupported tab resize mode "%s".', char(string(mode)));
+    end
     rows = rows(rows >= 1 & rows < logicalRows & isfinite(rows));
 end
 

+labkit/+ui/+app/tab.m

@@ -3,7 +3,7 @@
 %
 % Usage:
 %   spec = labkit.ui.app.tab('filesAnalysis', 'Files + Analysis', ...
-%       [4 1], {240, 220, 280, 160}, struct('resizeRows', [1 2 3]));
+%       [4 1], {240, 220, 280, 160});
 %
 % Inputs:
 %   key - valid field-name style identifier used in the returned ui struct.
@@ -14,7 +14,8 @@
 %
 % Options:
 %   columnWidth - cell row of column widths, default all {'1x'}.
-%   resizeRows - numeric logical-row boundaries after which drag handles are added.
+%   resize - row-resize behavior: 'betweenRows' default, or 'none'.
+%   resizeRows - legacy numeric logical-row boundaries. Prefer resize.
 %   resizeOptions - struct passed to row-resize handle creation.
 %   padding, rowSpacing, columnSpacing - grid layout properties.
 %
@@ -27,20 +28,27 @@
     if nargin < 5
         opts = struct();
     end
+    optsHasResize = isfield(opts, 'resize');
+    optsHasResizeRows = isfield(opts, 'resizeRows');
 
     spec = struct( ...
         'key', char(key), ...
         'title', char(titleText), ...
         'gridSize', gridSize, ...
         'rowHeight', {asCellRow(rowHeight)}, ...
         'columnWidth', {repmat({'1x'}, 1, gridSize(2))}, ...
+        'resize', 'betweenRows', ...
         'resizeRows', [], ...
         'resizeOptions', struct());
 
     fields = fieldnames(opts);
     for k = 1:numel(fields)
         spec.(fields{k}) = opts.(fields{k});
     end
+
+    if optsHasResizeRows && isempty(opts.resizeRows) && ~optsHasResize
+        spec.resize = 'none';
+    end
 end
 
 function value = asCellRow(value)

README.md

@@ -6,8 +6,8 @@
 [![MATLAB](https://img.shields.io/badge/MATLAB-apps-orange.svg)](https://www.mathworks.com/products/matlab.html)
 
 Focused MATLAB GUI apps for lab workflows in electrochemistry, DIC, image
-measurement, microscopy focus stacking, batch image cropping, and wearable
-biosignal review.
+measurement, microscopy focus stacking, image enhancement, batch image
+cropping, and wearable biosignal review.
 
 LabKit MATLAB Workbench is an app-first research workbench. Each workflow keeps
 its own launch command, app-owned calculations, plots, summaries, and exports.
@@ -21,7 +21,7 @@ processing.
 | --- | --- |
 | App-first workflows | Independent MATLAB GUI apps for daily lab tasks instead of one monolithic analysis launcher. |
 | Electrochemistry support | Gamry DTA loading, chrono overlays, CIC, CSC, VT resistance, EIS plotting, pulse handling, and CSV export paths. |
-| Image and DIC workflows | DIC preprocessing/postprocessing, curve measurement, calibrated scale bars, focus-stack fusion, and batch microscope image crops. |
+| Image and DIC workflows | DIC preprocessing/postprocessing, curve measurement, calibrated scale bars, focus-stack fusion, paper image enhancement, and batch microscope image crops. |
 | Wearable biosignals | ECG/table import, filtering, peak detection, event segments, templates, and SNR-style measurement summaries. |
 | Reusable foundation | Layered `labkit.ui`, GUI-free `labkit.dta`, and GUI-free `labkit.biosignal` facades. |
 | Guarded behavior | MATLAB build tasks, synthetic fixtures, architecture guardrails, and GitHub Actions CI. |
@@ -53,6 +53,8 @@ labkit_DICPostprocess_app
 % Image measurement and microscopy utilities
 labkit_CurvatureMeasurement_app
 labkit_FocusStack_app
+labkit_ImageEnhance_app
+labkit_ImageMatch_app
 labkit_BatchImageCrop_app
 
 % Wearable biosignal review
@@ -75,6 +77,8 @@ options, and export outputs when the selected app provides an export action.
 | `labkit_DICPostprocess_app` | Ncorr strain overlays, ROI summary, and colorbar export | Ncorr MAT, reference image, ROI mask | EXX/EYY overlays, summary CSV, colorbar files |
 | `labkit_CurvatureMeasurement_app` | Editable curve tracing, calibrated scale, length, and circle-fit curvature | Image files | Overlay PNG and curvature/length CSV |
 | `labkit_FocusStack_app` | Microscope focus-stack fusion into an all-in-focus image | Focus image folder or selected image files | Fused PNG, focus map PNG, summary CSV |
+| `labkit_ImageEnhance_app` | Stepwise brightness, contrast, clarity, color, and white-balance enhancement for figures | Image files | Enhanced images and processing manifest CSV |
+| `labkit_ImageMatch_app` | Reference-based white-balance, tone, and color-style matching for figure images | Image files | Matched images and processing manifest CSV |
 | `labkit_BatchImageCrop_app` | Fixed-size batch microscope crops with per-image center and rotation | Microscope image files | Cropped images and crop manifest CSV |
 | `labkit_ECGPrint_app` | ECG waveform preview, filtering, peak/segment SNR, and SNR-over-time display | MAT timetable, CSV, or TSV recordings | Segment SNR CSV and waveform PNG |
 

apps/dic/dic_postprocess/labkit_DICPostprocess_app.m

@@ -36,12 +36,10 @@
     workbenchOpts.tabs = [ ...
         labkit.ui.app.tab('filesAnalysis', 'Files + Analysis', [4 1], ...
             {240, 230, 260, 120}, ...
-            struct('resizeRows', [1 2 3], ...
-            'resizeOptions', struct('minTopHeight', 120, 'minBottomHeight', 90))), ...
+            struct('resizeOptions', struct('minTopHeight', 120, 'minBottomHeight', 90))), ...
         labkit.ui.app.tab('summaryResults', 'Summary + Results', [2 1], ...
             {210, '1x'}, ...
-            struct('resizeRows', 1, ...
-            'resizeOptions', struct('minTopHeight', 120, 'minBottomHeight', 90))), ...
+            struct('resizeOptions', struct('minTopHeight', 120, 'minBottomHeight', 90))), ...
         labkit.ui.app.tab('log', 'Log', [1 1], {'1x'})];
     ui = labkit.ui.app.createShell(struct( ...
         'title', 'DIC Strain Postprocess', ...

apps/dic/dic_preprocess/+dic_preprocess/+ui/createLayout.m

@@ -13,12 +13,10 @@
     workbenchOpts.tabs = [ ...
         labkit.ui.app.tab('filesAnalysis', 'Files + Analysis', [4 1], ...
             {240, 210, 330, 170}, ...
-            struct('resizeRows', [1 2 3], ...
-            'resizeOptions', struct('minTopHeight', 120, 'minBottomHeight', 80))), ...
+            struct('resizeOptions', struct('minTopHeight', 120, 'minBottomHeight', 80))), ...
         labkit.ui.app.tab('summaryResults', 'Summary + Results', [2 1], ...
             {150, '1x'}, ...
-            struct('resizeRows', 1, ...
-            'resizeOptions', struct('minTopHeight', 90, 'minBottomHeight', 90))), ...
+            struct('resizeOptions', struct('minTopHeight', 90, 'minBottomHeight', 90))), ...
         labkit.ui.app.tab('log', 'Log', [1 1], {'1x'})];
     ui = labkit.ui.app.createShell(struct( ...
         'title', 'DIC Image Preprocess', ...

apps/image_measurement/batch_crop/labkit_BatchImageCrop_app.m

@@ -29,11 +29,9 @@
         'rightRowHeight', {{'1x'}});
     workbenchOpts.tabs = [ ...
         labkit.ui.app.tab('filesAnalysis', 'Files + Analysis', [3 1], ...
-            {250, 260, 145}, ...
-            struct('resizeRows', [1 2])), ...
+            {250, 260, 145}), ...
         labkit.ui.app.tab('summaryResults', 'Summary + Results', [2 1], ...
-            {240, '1x'}, ...
-            struct('resizeRows', 1)), ...
+            {240, '1x'}), ...
         labkit.ui.app.tab('log', 'Log', [1 1], {'1x'})];
 
     ui = labkit.ui.app.createShell(struct( ...

apps/image_measurement/curvature/+curvature/+ui/appShellOptions.m

@@ -11,10 +11,8 @@
     opts.tabs = [ ...
         labkit.ui.app.tab('filesAnalysis', 'Files + Analysis', [5 1], ...
             {140, 105, 355, 225, 160}, ...
-            struct('resizeRows', [1 2 3 4], ...
-            'resizeOptions', struct('minTopHeight', 140, 'minBottomHeight', 90))), ...
+            struct('resizeOptions', struct('minTopHeight', 140, 'minBottomHeight', 90))), ...
         labkit.ui.app.tab('summaryResults', 'Summary + Results', [2 1], ...
-            {170, '1x'}, ...
-            struct('resizeRows', 1)), ...
+            {170, '1x'}), ...
         labkit.ui.app.tab('log', 'Log', [1 1], {'1x'})];
 end

apps/image_measurement/focus_stack/labkit_FocusStack_app.m

@@ -33,11 +33,9 @@
     workbenchOpts.tabs = [ ...
         labkit.ui.app.tab('filesAnalysis', 'Files + Analysis', [4 1], ...
             {250, 235, 185, 170}, ...
-            struct('resizeRows', [1 2 3], ...
-            'resizeOptions', struct('minTopHeight', 130, 'minBottomHeight', 90))), ...
+            struct('resizeOptions', struct('minTopHeight', 130, 'minBottomHeight', 90))), ...
         labkit.ui.app.tab('summaryResults', 'Summary + Results', [2 1], ...
-            {220, '1x'}, ...
-            struct('resizeRows', 1)), ...
+            {220, '1x'}), ...
         labkit.ui.app.tab('log', 'Log', [1 1], {'1x'})];
 
     ui = labkit.ui.app.createShell(struct( ...

apps/image_measurement/image_enhance/+image_enhance/+export/buildManifest.m

@@ -0,0 +1,27 @@
+% Expected caller: image_enhance.export.writeOutputs and tests. Input is a
+% result struct array from batch export. Output is the stable CSV manifest table.
+function T = buildManifest(results)
+
+    results = results(:);
+    sourceImage = strings(numel(results), 1);
+    outputImage = strings(numel(results), 1);
+    status = strings(numel(results), 1);
+    widthPx = zeros(numel(results), 1);
+    heightPx = zeros(numel(results), 1);
+    stepCount = zeros(numel(results), 1);
+    message = strings(numel(results), 1);
+
+    for k = 1:numel(results)
+        sourceImage(k) = results(k).sourcePath;
+        outputImage(k) = results(k).outputPath;
+        status(k) = results(k).status;
+        widthPx(k) = results(k).widthPx;
+        heightPx(k) = results(k).heightPx;
+        stepCount(k) = results(k).stepCount;
+        message(k) = results(k).message;
+    end
+
+    T = table(sourceImage, outputImage, status, widthPx, heightPx, ...
+        stepCount, message, 'VariableNames', {'SourceImage', 'OutputImage', ...
+        'Status', 'Width_px', 'Height_px', 'StepCount', 'Message'});
+end

apps/image_measurement/image_enhance/+image_enhance/+export/writeOutputs.m

@@ -0,0 +1,103 @@
+% Expected caller: labkit_ImageEnhance_app and image_enhance export tests.
+% Inputs are loaded image items, ordered enhancement steps, and export options.
+% Output includes per-image result structs and the manifest CSV path.
+function payload = writeOutputs(items, steps, opts)
+
+    if isempty(items)
+        error('labkit_ImageEnhance_app:NoImagesLoaded', ...
+            'Load images before exporting enhanced outputs.');
+    end
+    if nargin < 3 || isempty(opts)
+        opts = struct();
+    end
+    outputFolder = optionValue(opts, 'outputFolder', string(pwd));
+    outputFormat = optionValue(opts, 'format', 'PNG');
+
+    if exist(outputFolder, 'dir') ~= 7
+        mkdir(outputFolder);
+    end
+
+    images = cell(numel(items), 1);
+    for k = 1:numel(items)
+        images{k} = items(k).image;
+    end
+    processed = image_enhance.ops.applyPipeline(images, steps);
+
+    resultTemplate = emptyResult();
+    results = repmat(resultTemplate, numel(items), 1);
+    for k = 1:numel(items)
+        result = resultTemplate;
+        result.sourcePath = items(k).path;
+        result.stepCount = numel(steps);
+        result.widthPx = size(processed{k}, 2);
+        result.heightPx = size(processed{k}, 1);
+
+        outputPath = uniqueOutputPath(outputFolder, items(k).path, outputFormat);
+        result.outputPath = outputPath;
+        try
+            imwrite(processed{k}, outputPath);
+            result.status = "saved";
+            result.message = "Saved";
+        catch ME
+            result.status = "failed";
+            result.message = string(ME.message);
+        end
+        results(k) = result;
+    end
+
+    manifestPath = uniquePath(fullfile(char(outputFolder), ...
+        'image_enhance_manifest.csv'));
+    writetable(image_enhance.export.buildManifest(results), manifestPath);
+
+    payload = struct();
+    payload.results = results;
+    payload.manifestPath = string(manifestPath);
+end
+
+function value = optionValue(opts, name, defaultValue)
+    value = defaultValue;
+    if isfield(opts, name) && ~isempty(opts.(name))
+        value = opts.(name);
+    end
+end
+
+function result = emptyResult()
+    result = struct( ...
+        'sourcePath', "", ...
+        'outputPath', "", ...
+        'status', "pending", ...
+        'widthPx', 0, ...
+        'heightPx', 0, ...
+        'stepCount', 0, ...
+        'message', "");
+end
+
+function outputPath = uniqueOutputPath(outputFolder, sourcePath, formatName)
+    [~, base, ~] = fileparts(char(sourcePath));
+    extension = formatExtension(formatName);
+    outputPath = uniquePath(fullfile(char(outputFolder), ...
+        sprintf('%s_enhanced%s', base, extension)));
+    outputPath = string(outputPath);
+end
+
+function extension = formatExtension(formatName)
+    switch upper(string(formatName))
+        case "TIFF"
+            extension = '.tif';
+        case "JPEG"
+            extension = '.jpg';
+        otherwise
+            extension = '.png';
+    end
+end
+
+function path = uniquePath(path)
+    [folder, base, ext] = fileparts(path);
+    candidate = fullfile(folder, [base ext]);
+    index = 1;
+    while isfile(candidate)
+        candidate = fullfile(folder, sprintf('%s_%03d%s', base, index, ext));
+        index = index + 1;
+    end
+    path = candidate;
+end

apps/image_measurement/image_enhance/+image_enhance/+io/imageDialogFilter.m

@@ -0,0 +1,9 @@
+% Expected caller: labkit_ImageEnhance_app file dialogs. Output is a uigetfile
+% filter spec for image formats supported by the enhancement workflow.
+function filterSpec = imageDialogFilter()
+
+    filterSpec = { ...
+        '*.png;*.jpg;*.jpeg;*.tif;*.tiff;*.bmp', ...
+        'Image files (*.png, *.jpg, *.jpeg, *.tif, *.tiff, *.bmp)'; ...
+        '*.*', 'All files (*.*)'};
+end

apps/image_measurement/image_enhance/+image_enhance/+io/readImages.m

@@ -0,0 +1,33 @@
+% Expected caller: labkit_ImageEnhance_app and batch export tests. Input is a
+% string vector of image paths. Output is an item struct array with RGB double
+% images normalized to [0, 1]. Alpha channels are ignored.
+function items = readImages(paths)
+
+    paths = string(paths(:));
+    template = image_enhance.state.emptyItem();
+    items = repmat(template, numel(paths), 1);
+
+    for k = 1:numel(paths)
+        imageData = imread(paths(k));
+        items(k) = template;
+        items(k).path = paths(k);
+        items(k).name = displayName(paths(k));
+        items(k).image = normalizeImage(imageData);
+    end
+end
+
+function name = displayName(path)
+    [~, base, ext] = fileparts(char(path));
+    name = string([base ext]);
+end
+
+function imageData = normalizeImage(imageData)
+    if ndims(imageData) == 2
+        imageData = repmat(imageData, 1, 1, 3);
+    elseif size(imageData, 3) > 3
+        imageData = imageData(:, :, 1:3);
+    end
+
+    imageData = im2double(imageData);
+    imageData = min(max(imageData, 0), 1);
+end